DeepImageSpam: Deep Learning based Image Spam Detection
This addresses the issue of image spam detection for internet users, but it is incremental as it applies a standard deep learning method to a specific domain.
The paper tackled the problem of detecting image spam, where spammers alter images to deceive users, by proposing a deep learning approach using convolutional neural networks. The method achieved an accuracy of 91.7% on a dataset of 810 natural and 928 spam images, outperforming existing techniques.
Hackers and spammers are employing innovative and novel techniques to deceive novice and even knowledgeable internet users. Image spam is one of such technique where the spammer varies and changes some portion of the image such that it is indistinguishable from the original image fooling the users. This paper proposes a deep learning based approach for image spam detection using the convolutional neural networks which uses a dataset with 810 natural images and 928 spam images for classification achieving an accuracy of 91.7% outperforming the existing image processing and machine learning techniques